Our goal in this project is to develop a systematic approach for estimating genetic and non-genetic components of variance for quantitative traits from pedigree data, while assessing the validity of a given model for the data at hand. Estimating variance components under a general multifactorial model in this manner represents an important early step in the study of possible genetic mechanisms which may underlie the familial aggregation seen in many common diseases. This project will consist of four phases which will complement one another: (1) A rigorous description of the asymtotic properties of multivariate models for pedigrees; (2) Robustness studies of maximum likelihood estimators obtained under these models for finite samples with non-normal distributions; (3) Evaluation of various goodness-of-fit statistics for assessing the goodness and stability of fit to real and simulated data sets; and (4) Application of these three approaches to detect possible etiological heterogeneity (genetic and non-genetic) in sets of pedigrees. This last phase has great potential in studies of many common diseases where no single etiological mechanism can account for the familial aggregation in either the disease or in traits associated with the disease. Two sets of pedigree data will be used to develop and test these techniques: a set of families with serum lipid measurements collected by the Lipid Research Clinic and a set of families with serum IgE measurements collected as part of a study on atopic allergies. Simulated pedigrees will also be used to illustrate selected features of these methods.